CN116862237A - Risk control method and system for lottery behaviors of user - Google Patents
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Abstract
The invention relates to a risk control method and a system for lottery behaviors of a user, which comprise the following steps: s1, taking a user participating in lottery drawing activities as an initial node; s2, acquiring the characteristics corresponding to each initial node and the evidence weight corresponding to each characteristic; s3, adding all evidence weights of each initial node to obtain comprehensive evidence weights of the initial node; and S4, performing risk control according to the comprehensive evidence weight of the initial node. According to the invention, the relation between the lottery behavior and the malicious behavior implementation risk is established according to the evidence weight of the characteristics when the user implements the lottery behavior, and the possibility of the malicious behavior implementation of the user can be evaluated by a non-technical person through the finally obtained risk value.
Description
Technical Field
The invention relates to the technical field of big data analysis, in particular to a risk control method and a risk control system for lottery behaviors of users.
Background
The wind control system generally performs system design, data acquisition and model training according to own business scenes by each enterprise so as to achieve the purposes of risk identification and control.
For certain specific business activities, non-technicians (such as operators, sales personnel, etc.) following the activity also need to perform risk assessment and adjustment on the activity situation, and conventional wind control systems cannot provide non-technicians with a guiding wind control model scoring standard.
Disclosure of Invention
The invention aims to provide a risk control method and a risk control system for lottery behaviors of a user, which are used for establishing a relation between the lottery behaviors and risks of malicious behaviors according to evidence weights of features when the user conducts the lottery behaviors, wherein non-technical personnel can evaluate the possibility of the malicious behaviors conducted by the user through finally obtained risk values.
In order to achieve the above purpose, the present invention provides the following technical solutions:
in one aspect, a risk control method for a user lottery behavior is provided, including the steps of:
s1, taking a user participating in lottery drawing activities as an initial node;
s2, acquiring the characteristics corresponding to each initial node and the evidence weight corresponding to each characteristic;
s3, adding all evidence weights of each initial node to obtain comprehensive evidence weights of the initial node;
and S4, performing risk control according to the comprehensive evidence weight of the initial node.
In another aspect, there is also provided a risk control system for implementing the risk control method, including:
the evidence weight calculation unit is used for acquiring the characteristics corresponding to each initial node and the evidence weight corresponding to each characteristic;
a comprehensive evidence weight calculation unit for adding all evidence weights of each initial node to obtain a comprehensive evidence weight of the initial node;
and the risk control unit is used for performing risk control according to the comprehensive evidence weight of the initial node.
In another aspect, an electronic device is provided, including a processor, a memory, a communication bus, a communication interface, and a computer program stored in the memory and executable on the processor, where the computer program is a risk control method program for a user lottery behavior described above.
In summary, compared with the prior art, the invention has the following beneficial effects:
according to the invention, the relation between the feature and the malicious behavior implementation Risk is established according to the evidence weight of the feature when the user implements the lottery behavior, so that the importance of each feature for judging the malicious behavior can be evaluated by calculating the evidence weight of each feature, and in addition, the possibility of the malicious behavior implementation of the user can be evaluated by a non-technical person through the finally obtained Risk value Risk, and the Risk control of the user behavior is increased without professional knowledge.
Drawings
Fig. 1 is a flowchart illustrating steps of a risk control method for a user lottery action according to the present invention.
FIG. 2 is a graph of age versus bad_i/bad_T in the present invention.
FIG. 3 is a graph of evidence weight for age versus bad_i/bad_T in the present invention.
Fig. 4 is a schematic structural diagram of a risk control system for user lottery behavior in the present invention.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a risk control method for a user lottery action in the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making creative efforts based on the embodiments of the present invention are included in the protection scope of the present invention.
Example 1
As shown in fig. 1, the present embodiment provides a risk control method for a user lottery, which includes the following steps:
s1, taking users participating in lottery activities as initial nodes, and marking all the users as Good users (namely Good);
in this embodiment, the users may be all users participating in the lottery activities, or may be part of the users, and when a part of the users are used as initial nodes, the part of the users may be determined by preset screening conditions (such as age, region, etc.), or may be determined by a random selection manner, and the number of users used as initial nodes may be determined according to the size of risk monitoring required, and meanwhile, because no behavioral analysis is performed on the users, in this step, it is assumed that the users used as initial nodes are all good users and marked;
further, the good user refers to a user who does not implement malicious behaviors, that is, a user who implements good behaviors, wherein the malicious behaviors refer to users who do not participate in lottery activities according to regulations, including but not limited to filling one or more of invalid mobile phone numbers, invalid identity card numbers, invalid communication addresses and the like, the invalidation can be automatically detected and judged by a system, including but not limited to missing numbers/characters (such as 17 digits of the identity card numbers), misplugging numbers/characters (such as digits of mobile phone numbers which are not 1 at the first, misplugging of wuhan cities as Wu Hanshi and the like), adding numbers/characters (such as 12 digits of mobile phone numbers and the like), and other data modes (such as 13800000000 and the like) which obviously do not accord with industry specifications, and it is required that the malicious behaviors can be automatically and objectively judged through the system, and the judgment result is not in dispute, and subjective judgment is not required; the good behavior refers to the behavior of the user in the lottery activity, except for the malicious behavior;
s2, acquiring the characteristics corresponding to each initial node and the evidence weight corresponding to each characteristic;
in this embodiment, the feature is one or more of a mobile phone number, a communication address, an identification card number, and the like provided when the user participates in the lottery;
further, in this embodiment, the evidence weight corresponding to each feature is obtained by using formula (1):
W_i＝ln(Bad_i/Bad_T)-ln(Good_i/Good_T) (1)
wherein W_i is evidence weight of a certain feature, and bad_i represents the number of samples of all malicious behaviors corresponding to the current feature of the current initial node; bad_t represents the number of samples of all malicious behavior; good_i represents the number of samples of all Good behaviors corresponding to the current feature of the current initial node, and good_t represents the number of samples of all Good behaviors;
the number of samples of all malicious behaviors refers to the total number of malicious behaviors of different types generated in all initial nodes (for example, 1000 users), and if the number of 3 malicious behaviors of an invalid mobile phone number, an invalid identity card number and an invalid communication address is 10 times, 20 times and 30 times in 1000 users, the number of samples bad_t of all malicious behaviors is the sum of the number of 3 malicious behaviors, namely 60 times, for the current initial node a, the number of samples bad_i of all malicious behaviors corresponding to the current initial node a is 10 times when the current initial node a implements the malicious behaviors of the invalid mobile phone number, under the characteristic of the mobile phone number;
similarly, the number of samples of all Good behaviors refers to the total number of different types of Good behaviors generated in all initial nodes (for example, 1000 users), and assuming that the number of 3 Good behaviors with valid mobile phone numbers, valid identity card numbers and no valid communication addresses is 15 times, 25 times and 35 times respectively in 1000 users, the number of samples good_t of all Good behaviors is the sum of the number of 3 Good behaviors, namely 75 times, for the current initial node a, the number of samples good_i of all Good behaviors corresponding to the current initial node a is 15 times when the current initial node a is used for realizing the Good behaviors with valid mobile phone numbers, and the number of samples good_i of all Good behaviors corresponding to the current initial node a is 15 times under the characteristic of mobile phone numbers;
specifically, table 1 shows the evidence weight calculation process of feature 1, feature 2, feature 3, feature 4, and feature 5 for a certain initial node B, where the "node behavior sample number" represents 150 behaviors implemented by the user including the initial node B, and for the "node behavior sample number" corresponding to each feature, bad_t is 50, and good_t is 100;
table 15 evidence weight calculation for features
| Features (e.g. a character) | Node behavior sample number | Good_i | Bad_i | Bad_i/Bad_T | Good_i/Good_T | W_i |
| 1 | 150 | 80 | 20 | 0.4 | 0.8 | -0.69 |
| 2 | 150 | 85 | 15 | 0.3 | 0.85 | -1.04 |
| 3 | 150 | 90 | 10 | 0.2 | 0.9 | -1.50 |
| 4 | 150 | 96 | 4 | 0.08 | 0.96 | -2.47 |
| 5 | 150 | 99 | 1 | 0.02 | 0.99 | -3.90 |
Further, for example, regarding the age, the relationship between the age and the malicious behavior cannot be intuitively obtained by counting the ages (the horizontal linear relationship in the lottery activity according to the actual situation;
after the age is replaced by the evidence weight w_i, as shown in fig. 3, the relationship between the evidence weight w_i (abscissa) and bad_i/bad_t (ordinate) is converted into a linear relationship, so that the larger the evidence weight w_i is, the larger the bad_i/bad_t is, and the greater the risk of malicious behavior is implemented; alternatively, considering feature 1, feature 2, feature 3, feature 4, and feature 5 in table 1 as unified features, it can be similarly seen that the larger bad_i/bad_t is, the larger evidence weight w_i is;
therefore, when the evidence weight W_i of a certain feature is larger, the implementation risk of malicious behaviors is higher, the feature can distinguish between the malicious behaviors and the good behaviors, and the significance of the feature in distinguishing whether the feature is the malicious behaviors is more important, so that the importance of each feature in judging the malicious behaviors can be evaluated by calculating the evidence weight of each feature;
s3, adding all evidence weights of each initial node to obtain comprehensive evidence weights of the initial node;
wherein the comprehensive evidence weight W Heald =w_1+w_2+ & gt w_i, wherein i is a positive integer representing the number of features;
s4, risk control is carried out according to the comprehensive evidence weight of the initial node;
specifically, the risk control according to the comprehensive evidence weight of the initial node comprises the following steps:
s41, setting an alert threshold T, and weighting the comprehensive evidence W of a certain initial node Heald Comparing with the warning threshold value, when W Heald When the warning threshold value T is not less than the warning threshold value T, the initial node is considered to be a high-risk user; in this embodiment, the alert threshold T has a value in the range of [0.4,1]]；
As described above, the evidence weights of individual features are positively correlated with the malicious behavior scale, so the aggregate evidence weight W obtained by the addition of the evidence weights of different features Heald The proportion of malicious behaviors is also positive correlation, when W Heald The probability of carrying out malicious behaviors by the user corresponding to the node is increased, namely the probability is increased, so that risk control is needed;
s42, repeating the step S41 to judge whether each initial node is a high risk user (namely, bad user);
s43, calculating a Risk value Risk of each high-Risk user according to the formula (2):
Risk＝1/(1+exp(-W heald ))(2)
Wherein exp represents a natural exponential function, the range of values of Risk is [0,1], and the closer the value of Risk is to 1, the higher the possibility that the high-Risk user implements malicious behavior is represented;
s44, taking Risk control measures for the high-Risk user according to the Risk value Risk, wherein the Risk control measures comprise one or more of service degradation, limiting access and the like, and specifically, the service degradation comprises not forwarding lottery requests of the high-Risk user, carrying out weight reduction and blackout on the high-Risk user, and the limiting access comprises rejecting system access requests of the high-Risk user and the like;
for example, in this embodiment, when the Risk value Risk is equal to or greater than 0.5, a Risk control measure is taken for the corresponding high Risk user.
In this embodiment, the relationship between the feature and the Risk of malicious behavior implementation is established according to the evidence weight of the feature when the user implements the lottery behavior, so that the relationship is a linear relationship, and therefore, the importance of each feature for judging the malicious behavior can be evaluated by calculating the evidence weight of each feature.
Example 2:
the present embodiment provides a risk control system for implementing the risk control method described in embodiment 1, as shown in fig. 4, including:
the evidence weight calculating unit 100 is configured to obtain a feature corresponding to each initial node and an evidence weight corresponding to each feature, and the steps are the same as S2;
a comprehensive evidence weight calculation unit 200, configured to add all evidence weights of each initial node to obtain a comprehensive evidence weight of the initial node, where the step is the same as S3;
and a risk control unit 300 for performing risk control according to the comprehensive evidence weight of the initial node, and the step is the same as S4.
Example 3:
as shown in fig. 5, the present embodiment provides a schematic structural diagram of an electronic device 1 for implementing a risk control method for a user lottery action.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as a risk control method program for a user lottery action.
The processor 10 may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing Unit, CPU), a microprocessor, a digital processing chip, a graphics processor, a combination of various control chips, and so on. The processor 10 is a Control Unit (Control Unit) of the electronic device 1, connects respective parts of the entire electronic device using various interfaces and lines, executes or executes programs or modules stored in the memory 11 (for example, a risk Control method program for executing a lottery action of a user, etc.), and invokes data stored in the memory 11 to perform various functions of the electronic device and process the data.
The memory 11 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may in other embodiments also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only for storing application software installed in an electronic device and various kinds of data, such as codes of a risk control method program for a lottery action of a user, etc., but also for temporarily storing data that has been output or is to be output.
The communication bus 12 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
The communication interface 13 is used for communication between the electronic device 1 and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
A risk control method program for a user lottery stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, and when executed in the processor 10, the risk control method for a user lottery described in embodiment 1 can be implemented.
Further, the present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement the risk control method for a user lottery behavior described in embodiment 1.
In summary, the invention establishes the relationship between the feature and the Risk of malicious behavior implementation according to the evidence weight of the feature when the user implements the lottery behavior, so that the importance of each feature for judging the malicious behavior can be evaluated by calculating the evidence weight of each feature.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A risk control method for a lottery behavior of a user, comprising the steps of:
s1, taking a user participating in lottery drawing activities as an initial node;
s2, acquiring the characteristics corresponding to each initial node and the evidence weight corresponding to each characteristic;
s3, adding all evidence weights of each initial node to obtain comprehensive evidence weights of the initial node;
and S4, performing risk control according to the comprehensive evidence weight of the initial node.
2. The risk control method of claim 1, wherein the characteristic is one or more of a cell phone number, a communication address, and an identification card number provided when the user participates in a lottery.
3. The risk control method of claim 1, wherein the evidence weight corresponding to each feature is obtained using formula (1):
W_i＝ln(Bad_i/Bad_T)-ln(Good_i/Good_T)(1)
wherein W_i is evidence weight of a certain feature, and bad_i represents the number of samples of all malicious behaviors corresponding to the current feature of the current initial node; bad_t represents the number of samples of all malicious behavior; good_i represents the number of samples of all Good behaviors corresponding to the current feature of the current initial node, and good_t represents the number of samples of all Good behaviors.
4. The risk control method of claim 1, wherein the user is determined by a preset screening condition or by a randomly selected manner.
5. The risk control method according to claim 1, wherein S4, performing risk control according to the comprehensive evidence weight of the initial node, includes the steps of:
s41, setting an alert threshold T, and weighting the comprehensive evidence W of a certain initial node Heald In contrast to the alert threshold value,
when W is Heald When the warning threshold value T is not less than the warning threshold value T, the initial node is considered to be a high-risk user;
s42, repeating the step S41 to judge whether each initial node is a high risk user;
s43, calculating a Risk value Risk of each high-Risk user according to the formula (2):
Risk＝1/(1+exp(-W heald ))(2)
Wherein exp represents a natural exponential function;
s44, taking Risk control measures for the high-Risk users according to the Risk value Risk.
6. The risk control method according to claim 5, wherein the alert threshold T has a value in the range of [0.4,1].
7. The Risk control method according to claim 5, wherein when the Risk value Risk is equal to or greater than 0.5, a Risk control measure is taken for the corresponding high Risk user.
8. The risk control method of claim 5, wherein the risk control measures include one or more of service degradation, restricted access.
9. A risk control system for implementing the risk control method of claim 1, comprising:
the evidence weight calculation unit is used for acquiring the characteristics corresponding to each initial node and the evidence weight corresponding to each characteristic;
a comprehensive evidence weight calculation unit for adding all evidence weights of each initial node to obtain a comprehensive evidence weight of the initial node;
and the risk control unit is used for performing risk control according to the comprehensive evidence weight of the initial node.
10. An electronic device comprising a processor, a memory, a communication bus and a communication interface, characterized in that the electronic device further comprises a computer program stored in the memory and executable on the processor, the computer program being a risk control method program of a user lottery action according to any one of claims 1-8.
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| CN108875388A (en) * | 2018-05-31 | 2018-11-23 | 康键信息技术（深圳）有限公司 | Real-time risk control method, device and computer readable storage medium |
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